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Hyper-parameter tuning for the (1+ (λ, λ)) GA

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gecco_version.pdf (1001.Kb)
Date
13/07/2019
Author
Dang, Nguyen
Doerr, Carola
Keywords
Hyper-parameter tuning
Genetic algorithm
Evolutionary algorithm
Algorithm configuration
QA75 Electronic computers. Computer science
Computer Science (miscellaneous)
NDAS
BDC
R2C
~DC~
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Abstract
It is known that the (1 + (λ, λ)) Genetic Algorithm (GA) with self-adjusting parameter choices achieves a linear expected optimization time on OneMax if its hyper-parameters are suitably chosen. However, it is not very well understood how the hyper-parameter settings influences the overall performance of the (1 + (λ, λ)) GA. Analyzing such multi-dimensional dependencies precisely is at the edge of what running time analysis can offer. To make a step forward on this question, we present an in-depth empirical study of the self-adjusting (1 + (λ, λ)) GA and its hyper-parameters. We show, among many other results, that a 15% reduction of the average running time is possible by a slightly different setup, which allows non-identical offspring population sizes of mutation and crossover phase, and more flexibility in the choice of mutation rate and crossover bias --- a generalization which may be of independent interest. We also show indication that the parametrization of mutation rate and crossover bias derived by theoretical means for the static variant of the (1 + (λ, λ)) GA extends to the non-static case.
Citation
Dang , N & Doerr , C 2019 , Hyper-parameter tuning for the (1+ (λ, λ)) GA . in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19) . ACM , New York , pp. 889-897 , The Genetic and Evolutionary Computation Conference (GECCO 2019 @ Prague) , Prague , Czech Republic , 13/07/19 . https://doi.org/10.1145/3321707.3321725
 
conference
 
Publication
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19)
DOI
https://doi.org/10.1145/3321707.3321725
Type
Conference item
Rights
©2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. This work has been made available online in accordance with the publisher's policies. This is the final published version of the work, which was originally published at https://doi.org/10.1145/3321707.3321725
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/18095

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